About Pasqal
PASQAL designs and develops Quantum Processing Units (QPUs) and associated software tools.
Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers; these cases can concern industrial application challenges as well as fundamental science needs.
In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry.
Job Description
As part of our strong growth, we are looking for an engineer with a strong background in Operations Research (Applied Mathematics), including exact and heuristic algorithms and graph theory, to reinforce our Quantum Graph Optimization (QGO) team.
In this role, you will primarily focus on designing, developing, and delivering client-facing projects based on PASQAL quantum processors. You will adapt existing solutions and co-develop new applications with clients, translating real-world optimization problems into graph-based and quantum-ready formulations. Your work will be strongly oriented toward practical use cases in graph theory, operations research, and related industrial applications.
You will act as a technical lead for client engagements, managing the technical relationship with existing customers and partners and leading applied R&D projects with prospective ones. This includes collaborating closely with clients and partners to define requirements, prototype solutions based on our quantum technology, and deliver results, as well as contributing to internal development efforts. You will work in close coordination with Engineering, R&D, and external partners across all project phases, from problem definition to deployment.
As a Senior Quantum Algorithm Developer, your missions and responsibilities will be as follows:
- Investigate realistic implementations in our neutral-atom hardware, mostly based on analog paradigms, testing realistic parameter settings and analyzing the limitations and potential of our technology in solving challenging, industrial optimization problems
- Collaborate with our industrial partners, and in some projects also work together with clients who possess domain-expertise on the use cases we work.
- Apply analytical tools in mathematics and computer science, to test the feasibility of the algorithmic methods in solving some of the most promising use cases.
- Investigate the risk and return for certain use cases, the limitations of classical computing in solving them, and the potential benefits of our quantum computers.
- Develop codes in collaboration with our software engineering team, running on a variety of special-purpose simulation/emulation backends locally, on our HPC cluster, and mostly using our QPUs.
- Provide help on parts of code for which they are not responsible if necessary
- Contribute to the activities of the team pertaining to Quantum Optimization, notably in the form of developing new algorithms and scientific watch
- Have an inventive activity in the scientific and technical fields related to the Company's research, products, technologies and markets; filing patents.
About you
With a MSc or a PhD in Operations Research or in a related field and with at least 5 years in a similar position, you have the following assets:
- Experience with at least one of the following optimization frameworks: SCIP, DIP, PuLP, DipPy, Pyomo, JuMP
- Experience with at least one of the following programming languages: C, C++, Python, CUDA, Julia
- Experience with linear and non-linear solvers such as Cplex, Baron, Gurobi, GLPK, IPOP
- Strong taste for Applied Mathematics and graphs, and a keen interest in deep tech and new technologies
- Good practices in algorithms development and numerical simulations
- Good practices in research and project management
- Report/documentation writing
Notions of quantum computing, atomic physics, and optics are not mandatory but highly appreciated if combined with the aforementioned skills.
What we offer
- Offices in Saudi Arabia - Riyadh (KACST), with travels to Dammam and Jeddah to be expected
- Travels to our HQ (Palaiseau, France) 1 week every 2 to 3 months
- A flexible rhythm of remote work (2 days per week)
- Type of contract: permanent
- A dynamic and close-knit international team
- A key role in a growing start-up
- Free time to train and go to conferences once a year
Recruitment process
- An interview with our People Acquisition Partner (~45').
- A technical interview with the Lead of the Quantum Optimization team (~1hr)
- A home assignment
- A team fit interview with a few people from the Optimization team (~2hrs - onsite or online)
- An offer
PASQAL is an equal opportunity employer. We are committed to creating a diverse and inclusive workplace, as inclusion and diversity are essential to achieving our mission. We encourage applications from all qualified candidates, regardless of gender, ethnicity, age, religion or sexual orientation.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
This role represents a critical translational function situated at the intersection of quantum hardware maturity and commercial application viability. It is structurally essential for converting industrial-scale optimization challenges—typically expressed in Operations Research (OR) frameworks—into graph-based, quantum-ready formulations executable on neutral-atom processors. The primary value-chain impact is the systemic reduction of Technology Readiness Level (TRL) mismatch, accelerating the shift from theoretical promise to validated, client-facing proofs-of-concept. This role anchors early enterprise adoption strategies by providing the technical expertise necessary to benchmark quantum-enabled solutions against classical supercomputing baselines, a key market signal for investment and scalability.
The quantum applications segment of the value chain is experiencing a strong pivot towards demonstrable advantage in constrained optimization, positioning algorithm developers with deep OR expertise as high-demand resources. Current macro constraints include a significant talent shortage in professionals capable of architecting robust hybrid classical-quantum workflows, particularly those managing the noise characteristics and limited connectivity of present-generation QPU architectures. While hardware continues to advance, the commercial bottleneck often resides in translational science—the effective mapping of complex, real-world problems (e.g., logistics, finance, resource allocation) onto specific quantum paradigms like analog simulation. The geographical context in Saudi Arabia highlights this role’s strategic importance, aligning quantum infrastructure development with large-scale industrial sectors actively seeking computational leverage to solve intractable resource management problems. Global investment trends confirm a market-wide shift in focus from pure research to delivering validated applications, making optimization specialists central to commercial viability assessments and strategic customer engagements where graph theory provides an established bridge to QPU utilization. Ongoing ecosystem initiatives aim to accelerate readiness for practical quantum applications.
The core technical architecture for this function necessitates a fusion of conventional Operations Research mastery with quantum-specific computational primitives. Domain expertise must span traditional optimization solvers (e.g., CPLEX, Gurobi, GLPK, SCIP) and high-performance implementation languages (Python, C++). This foundation is leveraged to orchestrate hybrid classical-quantum workflows, ensuring efficient pre- and post-processing of data within High-Performance Computing (HPC) environments for emulation and simulation. Specialized capability involves defining the precise graph formulation and subsequent mapping onto neutral-atom hardware controls, requiring an understanding of analog quantum programming paradigms and Hamiltonian engineering. This synthesis allows for rigorous benchmarking of quantum algorithms (such as QAOA or specific analog models) against established classical heuristics, providing the necessary data for technical viability studies and performance validation critical for enterprise-level deployment. * Accelerate the maturation of industrial optimization use cases leveraging quantum hardware.
* De-risk commercial adoption pathways by establishing validated performance benchmarks for specific problem classes.
* Translate high-level business objectives into precise quantum-ready graph formulations.
* Reduce the time-to-solution latency for complex computational bottlenecks in client supply chains.
* Advance the technical readiness level (TRL) of quantum computing applications across vertical markets.
* Enable the systematic comparison of quantum versus state-of-the-art classical solver performance.
* Drive the technical relationship required for joint R\&D projects with strategic enterprise partners.
* Document and standardize best practices for running graph optimization problems on QPU infrastructure.
* Inform hardware roadmaps by providing empirical feedback on algorithmic scalability and constraint handling.
* Cultivate a regional talent base capable of deploying hybrid quantum-classical optimization tools.
* Formalize intellectual property via inventive activity in quantum algorithm development and applied math.
* Establish validated software interfaces between industry-specific data sources and quantum programming environments.Industry Tags: Quantum Optimization, Neutral-Atom Computing, Operations Research, Quantum Algorithms, Applied Mathematics, Graph Theory, Hybrid Computing, Quantum Graph Optimization, High-Performance Computing (HPC)
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